RSNA Pulmonary Embolism Detection

The Radiological Society of North America Intracranial Aneurysm Detection (RSNA-ICA) dataset is a collection of over 4,000 CT brain scans annotated by a cohort of over 40 volunteer radiologists from RSNA and the American Society of Neuroradiology to show the presence and location of intracranial aneurysms. It also includes a set of about 200 imaging studies that are annotated with AI-generated segmentations highlighting abnormalities. The imaging data was provided by 18 institutions. Initially compiled in 2025 for the RSNA Intracranial Aneurysm Detection AI Challenge hosted on Kaggle competition platform (https://www.kaggle.com/competitions/rsna-intracranial-aneurysm-detection), it represents the largest publicly available collection of its kind. Additional information on the dataset and how to make use of it is provided in a forthcoming Data Resource Publication listed below, as well as on the Kaggle competition website, which also provides access to models developed during the c[...]

RSNA assembled this dataset in 2019 for the RSNA Intracranial Hemorrhage Detection AI Challenge (https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/). De-identified head CT studies were provided by four research institutions. A group of over 60 volunteer expert radiologists recruited by RSNA and the American Society of Neuroradiology labeled over 25,000 exams for the presence and subtype classification of acute intracranial hemorrhage.

The Radiological Society of North America Lumbar Spine Degenerative Classification dataset (RSNA-LSDD) is a collection of over 2,600 magnetic resonance imaging (MR) scans of the lumbar spine annotated by a cohort of about 60 volunteer radiologists recruited by the RSNA, the American Society for Spine Radiology and the American Society of Neuroradiology to identify the location and severity of five degenerative conditions across the five intervertebral disc levels (L1/L2, L2/L3, L3/L4, L4/L5, and L5/S1). The imaging data, comprising over 8,500 image series (Sagittal “T2”, Axial T2 and Sagittal T1), was provided by twelve institutions from across the globe. Initially compiled in 2024 for the RSNA Lumbar Spine Degenerative Classification AI Challenge hosted on Kaggle competition platform (https://www.kaggle.com/competitions/rsna-2024-lumbar-spine-degenerative-classification), it represents the largest publicly available collection of its kind. Additional information on the dataset an[...]

Blunt force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are key to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically by physical exam, patient symptoms, or laboratory tests. Prompt diagnosis of abdominal trauma using medical imaging is thus critical to patient care. AI tools that assist and expedite diagnosis of abdominal trauma have the potential to substantially improve patient care and health outcomes in the emergency setting. To create the ground truth dataset, RSNA collected imaging data sourced from 23 sites in 14 countries on six continents, including more than 4,000 CT exams with various abdomina[...]